Background:

Novel strategies are needed to make vaccine efficacy trials more robust given uncertain epidemiology of infectious disease outbreaks, such as arboviruses like Zika. Spatially resolved mathematical and statistical models can help investigators identify sites at highest risk of future transmission and prioritize these for inclusion in trials. Models can also characterize uncertainty in whether transmission will occur at a site, and how nearby or connected sites may have correlated outcomes. A structure is needed for how trials can use models to address key design questions, including how to prioritize sites, the optimal number of sites, and how to allocate participants across sites.

Methods:

We illustrate the added value of models using the motivating example of Zika vaccine trial planning during the 2015–2017 Zika epidemic. We used a stochastic, spatially resolved, transmission model (the Global Epidemic and Mobility model) to simulate epidemics and site-level incidence at 100 high-risk sites in the Americas. We considered several strategies for prioritizing sites (average site-level incidence of infection across epidemics, median incidence, probability of exceeding 1% incidence), selecting the number of sites, and allocating sample size across sites (equal enrollment, proportional to average incidence, proportional to rank). To evaluate each design, we stochastically simulated trials in each hypothetical epidemic by drawing observed cases from site-level incidence data.

Results:

When constraining overall trial size, the optimal number of sites represents a balance between prioritizing highest-risk sites and having enough sites to reduce the chance of observing too few endpoints. The optimal number of sites remained roughly constant regardless of the targeted number of events, although it is necessary to increase the sample size to achieve the desired power. Though different ranking strategies returned different site orders, they performed similarly with respect to trial power. Instead of enrolling participants equally from each site, investigators can allocate participants proportional to projected incidence, though this did not provide an advantage in our example because the top sites had similar risk profiles. Sites from the same geographic region may have similar outcomes, so optimal combinations of sites may be geographically dispersed, even when these are not the highest ranked sites.

Conclusion:

Mathematical and statistical models may assist in designing successful vaccination trials by capturing uncertainty and correlation in future transmission. Although many factors affect site selection, such as logistical feasibility, models can help investigators optimize site selection and the number and size of participating sites. Although our study focused on trial design for an emerging arbovirus, a similar approach can be made for any infectious disease with the appropriate model for the particular disease.

中文翻译：

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使用模拟传染病爆发为持续流行期间单独随机疫苗试验的地点选择和样本量提供信息

背景：

鉴于传染病爆发的流行病学不确定，例如寨卡病毒等虫媒病毒，需要新的策略来使疫苗功效试验更加有效。空间解析的数学和统计模型可以帮助研究人员确定未来传播风险最高的地点，并优先考虑将这些地点纳入试验。模型还可以表征传输是否会在某个站点发生的不确定性，以及附近或连接的站点可能如何产生相关结果。需要一个结构来说明试验如何使用模型来解决关键设计问题，包括如何确定站点的优先级、站点的最佳数量以及如何跨站点分配参与者。

方法：

我们使用 2015-2017 年寨卡病毒流行期间寨卡病毒疫苗试验计划的激励示例来说明模型的附加值。我们使用随机的、空间解析的传播模型（全球流行病和流动模型）来模拟美洲 100 个高风险地点的流行病和地点级发病率。我们考虑了多种策略来对站点进行优先排序（跨流行病的平均站点级感染发生率、中位发生率、发生率超过 1% 的概率）、选择站点数量以及跨站点分配样本量（平等注册，与平均发生率成正比，与等级成正比）。为了评估每个设计，我们通过从站点级别的发病率数据中提取观察到的案例，随机模拟了每个假设流行病中的试验。

结果：

在限制整体试验规模时，最佳站点数量代表了优先考虑最高风险站点和拥有足够站点以减少观察到太少端点的机会之间的平衡。尽管有必要增加样本大小以达到所需的功效，但无论目标事件数量如何，最佳位点数量都保持大致恒定。虽然不同的排名策略会返回不同的站点顺序，但它们在试用权方面的表现相似。研究人员可以根据预计的发生率按比例分配参与者，而不是从每个站点平均招募参与者，尽管这在我们的示例中没有提供优势，因为顶级站点具有相似的风险概况。来自同一地理区域的站点可能具有相似的结果，

结论：

数学和统计模型可以通过捕捉未来传播中的不确定性和相关性来帮助设计成功的疫苗接种试验。尽管影响站点选择的因素很多，例如后勤可行性，但模型可以帮助调查人员优化站点选择以及参与站点的数量和大小。尽管我们的研究侧重于针对一种新出现的虫媒病毒的试验设计，但对于任何传染病，都可以采用针对特定疾病的适当模型的类似方法。